Posts Tagged biomarker

Abstract

Background

Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner.

Methods

This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research.

Results

Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state.

Conclusions

The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.

Background

Deep Brain Stimulation (DBS) can be classified into open-loop (also known as conventional) and closed-loop (also known as adaptive) paradigms. Closed-loop DBS employs a sensor to record a signal linked to symptoms while open-loop DBS does not use a sensor for recording the brain condition; therefore, stimulation parameters including duration, amplitude, and frequency of the pulse train remain constant in open-loop DBS regardless of fluctuations in the disease state. The recorded signal is known as a biomarker and can have varying nature, e.g. bioelectric, physiologic, biochemical, etc. In the open-loop DBS, a specialist tracks the patient’s clinical state and manually programs the device in a trial-and-error based manner. Adjustments of stimulation parameters are not conducted in real-time based on the ongoing neurophysiological variations in the brain; therefore, adverse effects on the patient may be induced due to the brain overstimulation. On the other hand, in the closed-loop DBS, the stimulation pulses are delivered when the brain is in an abnormal state, or they are automatically and dynamically adjusted based on the variations in the recorded signal over the time. Figure 1 compares open-loop and closed-loop DBS and illustrates how they act in different brain states.

Fig. 1 Overview of open-loop DBS (a) versus closed-loop DBS (b). In open-loop DBS, a neurologist manually adjusts the stimulation parameters every 3–12 months after DBS implantation. On the other hand, in closed-loop DBS, programming of the stimulation parameters is performed automatically based on the measured biomarker. c Demonstration of two different brain states and the action of open-loop and closed-loop DBS. When the brain enters a specific state, it remains in that state for a short or long time. Closed-loop DBS gets deactivated when the brain enters the normal state. Open-loop DBS continues the stimulation regardless of the brain state

Although the conventional DBS is a successful therapy, the closed-loop DBS is potentially capable of further and more efficient improvements in neurological diseases. A systematic review of the clinical literature by Hamani et al. [1] stated that adjusting the stimulation parameters of DBS devices could reduce or abolish adverse effects reported in 142 (19%) of 737 Parkinson’s disease (PD) patients treated with subthalamic nucleus (STN) DBS. In addition, Rosin et al. [2] demonstrated the superior function of closed-loop DBS, which automatically adjusts the stimulation parameters, to alleviate PD symptoms. Moreover, Little et al. [3] indicated that motor scores in eight PD patients improved by 50% (blinded) and 66% (unblinded) during closed-loop DBS, which were 27% (p = 0.005) to 29% (p = 0.03) higher than that of open-loop DBS. Besides these therapeutic benefits, they reported 56% reduction in stimulation time, as well as a decrease in the energy requirement of the closed-loop DBS compared to open-loop DBS. Therefore, patients may also benefit from fewer surgeries for replacement of the neurostimulator battery as a result of less power consumption in non-continuous stimulations [3]. Little et al. [3] and Wu et al. [4] reported that in order to obtain similar results from open-loop and closed-loop DBS, 44% less electrical stimulation is required using closed-loop DBS, which means higher efficiency, fewer surgery numbers, lower power consumption, and longer battery lifespan.

Although DBS is a successful therapy, its operation mechanism is mainly uncertain. Hess et al. [5] explained how the temporal pattern of stimulations might have key information for clarification of the DBS mechanism. A recent short review [6] on the physiological mechanism of DBS suggests the “disruption hypothesis” in which abnormal information is prevented from flowing into the stimulation site as a result of DBS dissociation effect on input and output signals. However, it is still under debate and remains to be confirmed by more pre-clinical research. Another review by Herrington et al. [7] accounts several non-exclusive mechanisms for DBS that depend on the condition being treated and the stimulation target. Despite the existence of different theories on the DBS mechanism, there are still questions in regard to the closed-loop DBS. Does adaptive control of DBS alter the DBS mechanism? If yes, how does it alter the DBS mechanism? These questions deserve consideration in the future experimental studies.

This paper presents a comprehensive review of portable closed-loop DBS devices. While there exists a number of excellent reviews on closed-loop DBS systems [8, 9, 10, 11, 12, 13, 14, 15, 16], this work differs from the existing works as described in the following. Among the published reviews, ref. [8] mainly highlights the applications of closed-loop DBS in the rehabilitation of movement disorders. Ref. [12] mainly describes the benefits of closed-loop DBS which using local field potentials (LFPs) as the feedback biomarker. Ref. [13] mainly reviews DBS (both open-loop and closed-loop) in terms of neurological aspects and clinical benefits. Ref. [9] indicates the available biomarkers for closing the feedback loop, and gives control strategies for manipulating measured signals relating to PD patient clinical state. Ref. [10] concentrates on emerging techniques in DBS including new electrode design, new stimulation patterns, and novel targeting techniques. Ref [16] has mainly focused on selection of biomarker and its benefits and problems. Ref. [14] introduces adaptive DBS, and outlines some technological advances in DBS including stimulation type and patterns, energy harvesting, and methods for increasing life quality of patients. Similarly, ref. [15] reviews some technological advancement such as surgical targeting, DBS parameters programming, and electrode design. On the other hand, ref. [11] highlights a range of issues associated with closed-loop DBS including biomarker sensing and processing, DBS parameters programming, control algorithm, wireless telemetry, and device size and power consumption.

This paper, on the other hand, provides a comprehensive review of closed-loop DBS devices, and covers a wider range of issues and advancements associated with such devices including: (i) biomarker selection, (ii) DBS parameters programming, (iii) stimulation type and pattern, (iv) control algorithms, (v) concurrent stimulation and recording, (vi) portability, (vii) battery-less technique, (viii) user-friendly interface, and (x) remote monitoring and wireless telemetry. The paper combines the key features of the current reviews going beyond devices that are used for specific disorders or biomarkers. It covers closed-loop DBS devices reported in the latest research publications not included in the existing reviews. The paper gives a brief history of closed-loop DBS. Next, it discusses different biomarkers for closing the feedback loop. Then, it reviews the algorithms developed for controlling stimulation parameters. After that, it highlights the current challenges and limitations for implementing closed-loop DBS. Also, it reviews the technological developments in closed-loop DBS. Then, it describes commercial closed-loop DBS systems. After that, it compares research-based closed-loop DBS devices highlighting future design expectations, and giving future directions and recommendations on closed-loop DBS devices. […]

Abstract

The most difficult clinical questions in stroke rehabilitation are “What is this patient’s potential for recovery?” and “What is the best rehabilitation strategy for this person, given her/his clinical profile?” Without answers to these questions, clinicians struggle to make decisions regarding the content and focus of therapy, and researchers design studies that inadvertently mix participants who have a high likelihood of responding with those who do not. Developing and implementing biomarkers that distinguish patient subgroups will help address these issues and unravel the factors important to the recovery process. The goal of the present paper is to provide a consensus statement regarding the current state of the evidence for stroke recovery biomarkers. Biomarkers of motor, somatosensory, cognitive and language domains across the recovery timeline post-stroke are considered; with focus on brain structure and function, and exclusion of blood markers and genetics. We provide evidence for biomarkers that are considered ready to be included in clinical trials, as well as others that are promising but not ready and so represent a developmental priority. We conclude with an example that illustrates the utility of biomarkers in recovery and rehabilitation research, demonstrating how the inclusion of a biomarker may enhance future clinical trials. In this way, we propose a way forward for when and where we can include biomarkers to advance the efficacy of the practice of, and research into, rehabilitation and recovery after stroke.

Introduction

Stroke is a heterogeneous condition, making choice of treatment, and prediction of outcome and treatment response, difficult. Despite this, clinical trials are often designed with a ‘one size fits all’ point of view, which can make them vulnerable to patient heterogeneity, reduced statistical power, and thus failure. Biomarkers can greatly inform patient selection for trials in general medical research, and this is equally true for stroke recovery. A stroke recovery biomarker (SRB) can be defined as an indicator of disease state that can be used as a measure of underlying molecular/cellular processes that may be difficult to measure directly in humans, and could be used to understand outcome, or predict recovery or treatment response.1

In practical terms, biomarkers should improve our ability to predict long-term outcomes after stroke across multiple domains. This is beneficial for: (a) patients, caregivers and clinicians; (b) planning subsequent clinical pathways and goal setting; and (c) identifying whom and when to target, and in some instances at which dose, with interventions for promoting stroke recovery.2 This last point is particularly important as methods for accurate prediction of long-term outcome would allow clinical trials of restorative and rehabilitation interventions to be stratified based on the potential for neurobiological recovery in a way that is currently not possible when trials are performed in the absence of valid biomarkers. Unpredictable outcomes after stroke, particularly in those who present with the most severe impairment3 mean that clinical trials of rehabilitation interventions need hundreds of patients to be appropriately powered. Use of biomarkers would allow incorporation of accurate information about the underlying impairment, and thus the size of these intervention trials could be considerably reduced,4 with obvious benefits. These principles are no different in the context of stroke recovery as compared to general medical research.5

Interventions fall into two broad mechanistic categories: (1) behavioural interventions that take advantage of experience and learning-dependent plasticity (e.g. motor, sensory, cognitive, and speech and language therapy), and (2) treatments that enhance the potential for experience and learning-dependent plasticity to maximise the effects of behavioural interventions (e.g. pharmacotherapy or non-invasive brain stimulation).6 To identify in whom and when to intervene, we need biomarkers that reflect the underlying biological mechanisms being targeted therapeutically.

Our goal is to provide a consensus statement regarding the evidence for SRBs that are helpful in outcome prediction and therefore identifying subgroups for stratification to be used in trials.7 We focused on SRBs that can investigate the structure or function of the brain (Table 1). Four functional domains (motor, somatosensation, cognition, and language (Table 2)) were considered according to recovery phase post stroke (hyperacute: <24 h; acute: 1 to 7 days; early subacute: 1 week to 3 months; late subacute: 3 months to 6 months; chronic: > 6 months8). For each functional domain, we provide recommendations for biomarkers that either are: (1) ready to guide stratification of subgroups of patients for clinical trials and/or to predict outcome, or (2) are a developmental priority (Table 3). Finally, we provide an example of how inclusion of a clinical trial-ready biomarker might have benefitted a recent phase III trial. As there is generally limited evidence at this time for blood or genetic biomarkers, we do not discuss these, but recommend they are a developmental priority.9–12 We also recognize that many other functional domains exist, but focus here on the four that have the most developed science. […]

What determines motor recovery in stroke is still unknown and finding markers that could predict and improve stroke recovery is a challenge. In this study, we aimed at understanding the neural mechanisms of motor function recovery after stroke using neurophysiological markers by means of cortical excitability (Transcranial Magnetic Stimulation – TMS) and brain oscillations (electroencephalography – EEG). In this cross-sectional study, fifty-five subjects with chronic stroke (62±14 yo, 17 women, 32±42 months post-stroke) were recruited in two sites. We analyzed TMS measures (i.e. motor threshold – MT – of the affected and unaffected sides) and EEG variables (i.e. power spectrum in different frequency bands and different brain regions of the affected and unaffected hemispheres) and their correlation with motor impairment as measured by Fugl-Meyer. Multiple univariate and multivariate linear regression analyses were performed to identify the predictors of good motor function. A significant interaction effect of MT in the affected hemisphere and power in beta bandwidth over the central region for both affected and unaffected hemispheres was found. We identified that motor function positively correlates with beta rhythm over the central region of the unaffected hemisphere, while it negatively correlates with beta rhythm in the affected hemisphere. Our results suggest that cortical activity in the affected and unaffected hemisphere measured by EEG provides new insights on the association between high frequency rhythms and motor impairment, highlighting the role of excess of beta in the affected central cortical region in poor motor function in stroke recovery.

Introduction

Stroke is a leading cause of morbidity, mortality, and disability worldwide (1, 2). Among the sequels of stroke, motor impairment is one of the most relevant, since it conditions the quality of life of patients, it reduces their capability to perform their daily activities and it impairs their autonomy (3). Despite the advancements of the acute stroke therapy, patients require an intensive rehabilitation program that will partially determine the extent of their recovery (4). These rehabilitation programs aim at stimulating cortical plasticity to improve motor performance and functional recovery (5). However, what determines motor improvement is still unknown. Indeed, finding markers that could predict and enhance stroke recovery is still a challenge (6). Different types of biomarkers exist: diagnostic, prognostic, surrogate outcome, and predictive biomarkers (7). The identification of these biomarkers is critical in the management of stroke patients. In the field of stroke research, great attention has been put to biomarkers found in the serum, especially in acute care. However, research on biomarkers of stroke recovery is still limited, especially using neurophysiological tools.

A critical research area in stroke is to understand the neural mechanisms underlying motor recovery. In this context, neurophysiological techniques such as transcranial magnetic stimulation (TMS) and electroencephalography (EEG) are useful tools that could be used to identify potential biomarkers of stroke recovery. However, there is still limited data to draw further conclusions on neural reorganization in human trials using these techniques. A few studies have shown that, in acute and sub-acute stage, stroke patients present increased power in low frequency bands (i.e., delta and theta bandwidths) in both affected and unaffected sides, as well as increased delta/alpha ratio in the affected brain area; these patterns being also correlated to functional outcome (8–11). Recently, we have identified that, besides TMS-indexed motor threshold (MT), an increased excitability in the unaffected hemisphere, coupled with a decreased excitability in the affected hemisphere, was associated with poor motor function (12), as measured by Fugl-Meyer (FM) [assessing symptoms severity and motor recovery in post-stroke patients with hemiplegia—Fugl-Meyer et al. (13); Gladstone et al. (14)]. However, MT measurement is associated with a poor resolution as it indexes global corticospinal excitability. Therefore, combining this information with direct cortical measures such as cortical oscillations, as measured by EEG, can help us to understand further neural mechanisms of stroke recovery.

To date, there are very few studies looking into EEG and motor recovery. For that reason, we aimed, in the present study, to investigate the relationship between motor impairment, EEG, and TMS variables. To do so, we conducted a prospective multicenter study of patients who had suffered from a stroke, in which we measured functional outcome using FM and performed TMS and EEG recordings. Based on our preliminary work, we expected to identify changes in interhemispheric imbalances on EEG power, especially in frequency bands associated with learning, such as alpha and beta bandwidths. […]

Figure 1. Topoplots showing the topographic distribution of high-beta bandwidth (25 Hz) for every individual. Red areas represent higher high-beta activity, while blue areas represent lower high-beta activity. Central region (C3 or C4) in red stands for the affected side. For patients with poor motor function, a higher beta activity of the affected central region as compared to the affected side is observed in 16 out of 28 individuals. For patients with good motor function, a similar activity over central regions bilaterally, or higher activity over the unaffected central area can be identified in 21 out of 27 individuals. FM = Fugl-Meyer.